9 citations · 25 across the 22 of their papers we have counts for
5 papers · 1 filter
Get rid of your constraints and reparametrize: A study in NNLS and implicit bias
Hung-Hsu Chou, Johannes Maly, Claudio Mayrink Verdun +2
Over the past years, there has been significant interest in understanding the implicit bias of gradient descent optimization and its connection to the generalization properties of…
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
Christian Kümmerle, Claudio Mayrink Verdun
We propose an iterative algorithm for low-rank matrix completion that can be interpreted as an iteratively reweighted least squares (IRLS) algorithm, a saddle-escaping smoothing Ne…
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate
Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger
The recovery of sparse data is at the core of many applications in machine learning and signal processing. While such problems can be tackled using -regularization as in th…
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method
Christian Kümmerle, Claudio M. Verdun
We propose an iterative algorithm for low-rank matrix completion that can be interpreted as both an iteratively reweighted least squares (IRLS) algorithm and a saddle-escaping smoo…
Denoising and Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squares
Christian Kümmerle, Claudio Mayrink Verdun
We propose a new Iteratively Reweighted Least Squares (IRLS) algorithm for the problem of completing or denoising low-rank matrices that are structured, e.g., that possess a Hankel…